Integration of Machine Learning and Web-Based Expert Systems for Diabetes Risk Analysis in Pagar Alam
DOI:
https://doi.org/10.30983/knowbase.v5i2.10268Keywords:
Machine Learning, Expert system, data analyst, Diabetes Machine Learning Expert System Genomics CRISP-DM SVM, Diabetes, Machine Learning, Expert System,Genomics, CRISP-DM, SVMAbstract
This study aims to develop an integrated system combining Machine Learning (ML) and a Web-Based Expert System for genomic and clinical data analysis to mitigate the rising diabetes cases in Pagar Alam City. The research adopts the CRISP-DM (Cross-Industry Standard Process for Data Mining) methodology, encompassing business understanding, data understanding, data preparation, modeling, evaluation, and deployment phases. Unlike previous studies relying on standard public datasets, this research integrates genomic profiles (TCF7L2 and KCNQ1 SNPs) alongside local clinical parameters from five sub-districts in Pagar Alam. Quantitative data from 640 samples were analyzed using the Support Vector Machine (SVM) algorithm. Evaluation results during the modeling phase show that the SVM model achieved a superior accuracy of 99.07%, demonstrating that integrating genomic data significantly enhances predictive precision. The web-based expert system implemented in the deployment phase provides personalized prevention recommendations based on individual risk profiles. This application is expected to serve as a strategic tool for the Pagar Alam government to enhance the effectiveness of prevention programs through localized and genetic-based interventions.
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